Thesis Topic Details
Topic ID: |
3235 | |
Title: |
Phenotype Prediction with Models of Cellular Systems | |
Supervisor: |
Mike Bain | |
Research Area: |
Bioinformatics, Biomedical Engineering, Modelling | |
| Associated Staff | ||
|---|---|---|
Assessor: |
Mark Temple | |
| Topic Details | ||
Status: |
Active | |
Type: |
Research | |
Programs: |
BIOM BINF | |
Group Suitable: |
No | |
Industrial: |
No | |
Pre-requisites: |
-- | |
Description: |
Project overview: Gene function is typically meaningful only as part of a molecular network or ``circuit'' in the cell. This has led to systems biology, in which responsive phenotypes, the measurable characteristics of the organism in response to environmental or genetic perturbations, can be investigated genome-wide, i.e., by collecting data on the activity of all the organism's genes simultaneously. Modelling cellular networks is hard; too little is known about the quantitative dynamics of all the relevant reactions in the system to obtain accurate predictions. However, approximating these systems using qualitative models, such as Boolean networks, can give useful insights, particularly how the dynamics of a network can vary under changed environmental conditions. This project will collect systems biology data on transcriptomics, proteomics, and other cellular activity, and model it using a selected qualitative approach to relate it to a novel set of phenotypes assembled for the yeast deletion library. In collaboration with yeast biologists the project will use bioinformatics and machine learning methods to simplify and structure the data and relate it to biological knowledge such as Gene Ontology, pathways, etc. Approach: Assemble data sets, design and implement DBMS, install machine learning tools, implement (minimalist) Web user-interface. In collaboration with yeast biologists, select subset of phenotypes and use machine learning and modelling to relate data to phenotypes. Assemble outputs in human-comprehensible format to enable biological validation, to lead to further hypothesis generation and modelling. Skills: Programming in scripting language (PHP, Perl, Ruby, etc.), familiarity with Java, database design and implementation (MySQL), simple Web interface design and implentation, familiarity with bioinformatics tools and algorithms. |
|
Comments: |
-- | |
| Past Student Reports | ||
| Sandeep KAUR in s1, 2012 Phenotype Prediction with Models of Cellular Systems |
||
| Djordje DJORDJEVIC in s2, 2012 Phenotype Prediction with Models of Cellular Systems |
||
Download report from the CSE Thesis Report Library NOTE: only current CSE students can login to view and select reports to download. | ||